# coding=utf-8
# Copyright 2022 SenseTime and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""PyTorch Deformable DETR model."""

import math
import warnings
from dataclasses import dataclass
from typing import Any, Optional, Union

import torch
import torch.nn.functional as F
from torch import Tensor, nn

from ... import initialization as init
from ...activations import ACT2FN
from ...integrations import use_kernel_forward_from_hub
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import meshgrid
from ...utils import (
    ModelOutput,
    auto_docstring,
    is_timm_available,
    logging,
    requires_backends,
)
from ...utils.backbone_utils import load_backbone
from .configuration_deformable_detr import DeformableDetrConfig


logger = logging.get_logger(__name__)


if is_timm_available():
    from timm import create_model


logger = logging.get_logger(__name__)


@use_kernel_forward_from_hub("MultiScaleDeformableAttention")
class MultiScaleDeformableAttention(nn.Module):
    def forward(
        self,
        value: Tensor,
        value_spatial_shapes: Tensor,
        value_spatial_shapes_list: list[tuple],
        level_start_index: Tensor,
        sampling_locations: Tensor,
        attention_weights: Tensor,
        im2col_step: int,
    ):
        batch_size, _, num_heads, hidden_dim = value.shape
        _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
        value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1)
        sampling_grids = 2 * sampling_locations - 1
        sampling_value_list = []
        for level_id, (height, width) in enumerate(value_spatial_shapes_list):
            # batch_size, height*width, num_heads, hidden_dim
            # -> batch_size, height*width, num_heads*hidden_dim
            # -> batch_size, num_heads*hidden_dim, height*width
            # -> batch_size*num_heads, hidden_dim, height, width
            value_l_ = (
                value_list[level_id]
                .flatten(2)
                .transpose(1, 2)
                .reshape(batch_size * num_heads, hidden_dim, height, width)
            )
            # batch_size, num_queries, num_heads, num_points, 2
            # -> batch_size, num_heads, num_queries, num_points, 2
            # -> batch_size*num_heads, num_queries, num_points, 2
            sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
            # batch_size*num_heads, hidden_dim, num_queries, num_points
            sampling_value_l_ = nn.functional.grid_sample(
                value_l_,
                sampling_grid_l_,
                mode="bilinear",
                padding_mode="zeros",
                align_corners=False,
            )
            sampling_value_list.append(sampling_value_l_)
        # (batch_size, num_queries, num_heads, num_levels, num_points)
        # -> (batch_size, num_heads, num_queries, num_levels, num_points)
        # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
        attention_weights = attention_weights.transpose(1, 2).reshape(
            batch_size * num_heads, 1, num_queries, num_levels * num_points
        )
        output = (
            (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
            .sum(-1)
            .view(batch_size, num_heads * hidden_dim, num_queries)
        )
        return output.transpose(1, 2).contiguous()


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of the DeformableDetrDecoder. This class adds two attributes to
    BaseModelOutputWithCrossAttentions, namely:
    - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
    - a stacked tensor of intermediate reference points.
    """
)
class DeformableDetrDecoderOutput(ModelOutput):
    r"""
    intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
        Stacked intermediate hidden states (output of each layer of the decoder).
    intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`):
        Stacked intermediate reference points (reference points of each layer of the decoder).
    cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
        Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
        sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
        used to compute the weighted average in the cross-attention heads.
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[tuple[torch.FloatTensor]] = None


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of the Deformable DETR encoder-decoder model.
    """
)
class DeformableDetrModelOutput(ModelOutput):
    r"""
    init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
        Initial reference points sent through the Transformer decoder.
    last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
        Sequence of hidden-states at the output of the last layer of the decoder of the model.
    intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
        Stacked intermediate hidden states (output of each layer of the decoder).
    intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked intermediate reference points (reference points of each layer of the decoder).
    enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are
        picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
        foreground and background).
    enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the first stage.
    """

    init_reference_points: Optional[torch.FloatTensor] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    decoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[tuple[torch.FloatTensor]] = None
    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    enc_outputs_class: Optional[torch.FloatTensor] = None
    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`DeformableDetrForObjectDetection`].
    """
)
class DeformableDetrObjectDetectionOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
        Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
        bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
        scale-invariant IoU loss.
    loss_dict (`Dict`, *optional*):
        A dictionary containing the individual losses. Useful for logging.
    logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
        Classification logits (including no-object) for all queries.
    pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
        Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
        values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
        possible padding). You can use [`~DeformableDetrProcessor.post_process_object_detection`] to retrieve the
        unnormalized bounding boxes.
    auxiliary_outputs (`list[Dict]`, *optional*):
        Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
        and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
        `pred_boxes`) for each decoder layer.
    init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
        Initial reference points sent through the Transformer decoder.
    last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
        Sequence of hidden-states at the output of the last layer of the decoder of the model.
    intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
        Stacked intermediate hidden states (output of each layer of the decoder).
    intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
        Stacked intermediate reference points (reference points of each layer of the decoder).
    enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are
        picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
        foreground and background).
    enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`):
        Logits of predicted bounding boxes coordinates in the first stage.
    """

    loss: Optional[torch.FloatTensor] = None
    loss_dict: Optional[dict] = None
    logits: Optional[torch.FloatTensor] = None
    pred_boxes: Optional[torch.FloatTensor] = None
    auxiliary_outputs: Optional[list[dict]] = None
    init_reference_points: Optional[torch.FloatTensor] = None
    last_hidden_state: Optional[torch.FloatTensor] = None
    intermediate_hidden_states: Optional[torch.FloatTensor] = None
    intermediate_reference_points: Optional[torch.FloatTensor] = None
    decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    decoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    cross_attentions: Optional[tuple[torch.FloatTensor]] = None
    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
    encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
    enc_outputs_class: Any = None
    enc_outputs_coord_logits: Optional[torch.FloatTensor] = None


def inverse_sigmoid(x, eps=1e-5):
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1 / x2)


# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->DeformableDetr
class DeformableDetrFrozenBatchNorm2d(nn.Module):
    """
    BatchNorm2d where the batch statistics and the affine parameters are fixed.

    Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
    torchvision.models.resnet[18,34,50,101] produce nans.
    """

    def __init__(self, n):
        super().__init__()
        self.register_buffer("weight", torch.ones(n))
        self.register_buffer("bias", torch.zeros(n))
        self.register_buffer("running_mean", torch.zeros(n))
        self.register_buffer("running_var", torch.ones(n))

    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        num_batches_tracked_key = prefix + "num_batches_tracked"
        if num_batches_tracked_key in state_dict:
            del state_dict[num_batches_tracked_key]

        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

    def forward(self, x):
        # move reshapes to the beginning
        # to make it user-friendly
        weight = self.weight.reshape(1, -1, 1, 1)
        bias = self.bias.reshape(1, -1, 1, 1)
        running_var = self.running_var.reshape(1, -1, 1, 1)
        running_mean = self.running_mean.reshape(1, -1, 1, 1)
        epsilon = 1e-5
        scale = weight * (running_var + epsilon).rsqrt()
        bias = bias - running_mean * scale
        return x * scale + bias


# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->DeformableDetr
def replace_batch_norm(model):
    r"""
    Recursively replace all `torch.nn.BatchNorm2d` with `DeformableDetrFrozenBatchNorm2d`.

    Args:
        model (torch.nn.Module):
            input model
    """
    for name, module in model.named_children():
        if isinstance(module, nn.BatchNorm2d):
            new_module = DeformableDetrFrozenBatchNorm2d(module.num_features)

            if module.weight.device != torch.device("meta"):
                new_module.weight.copy_(module.weight)
                new_module.bias.copy_(module.bias)
                new_module.running_mean.copy_(module.running_mean)
                new_module.running_var.copy_(module.running_var)

            model._modules[name] = new_module

        if len(list(module.children())) > 0:
            replace_batch_norm(module)


class DeformableDetrConvEncoder(nn.Module):
    """
    Convolutional backbone, using either the AutoBackbone API or one from the timm library.

    nn.BatchNorm2d layers are replaced by DeformableDetrFrozenBatchNorm2d as defined above.

    """

    def __init__(self, config):
        super().__init__()

        self.config = config

        # For backwards compatibility we have to use the timm library directly instead of the AutoBackbone API
        if config.use_timm_backbone:
            # We default to values which were previously hard-coded. This enables configurability from the config
            # using backbone arguments, while keeping the default behavior the same.
            requires_backends(self, ["timm"])
            kwargs = getattr(config, "backbone_kwargs", {})
            kwargs = {} if kwargs is None else kwargs.copy()
            out_indices = kwargs.pop("out_indices", (2, 3, 4) if config.num_feature_levels > 1 else (4,))
            num_channels = kwargs.pop("in_chans", config.num_channels)
            if config.dilation:
                kwargs["output_stride"] = kwargs.get("output_stride", 16)
            backbone = create_model(
                config.backbone,
                pretrained=config.use_pretrained_backbone,
                features_only=True,
                out_indices=out_indices,
                in_chans=num_channels,
                **kwargs,
            )
        else:
            backbone = load_backbone(config)

        # replace batch norm by frozen batch norm
        with torch.no_grad():
            replace_batch_norm(backbone)
        self.model = backbone
        self.intermediate_channel_sizes = (
            self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
        )

        backbone_model_type = None
        if config.backbone is not None:
            backbone_model_type = config.backbone
        elif config.backbone_config is not None:
            backbone_model_type = config.backbone_config.model_type
        else:
            raise ValueError("Either `backbone` or `backbone_config` should be provided in the config")

        if "resnet" in backbone_model_type:
            for name, parameter in self.model.named_parameters():
                if config.use_timm_backbone:
                    if "layer2" not in name and "layer3" not in name and "layer4" not in name:
                        parameter.requires_grad_(False)
                else:
                    if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name:
                        parameter.requires_grad_(False)

    # Copied from transformers.models.detr.modeling_detr.DetrConvEncoder.forward with Detr->DeformableDetr
    def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
        # send pixel_values through the model to get list of feature maps
        features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps

        out = []
        for feature_map in features:
            # downsample pixel_mask to match shape of corresponding feature_map
            mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
            out.append((feature_map, mask))
        return out


# Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->DeformableDetr
class DeformableDetrConvModel(nn.Module):
    """
    This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
    """

    def __init__(self, conv_encoder, position_embedding):
        super().__init__()
        self.conv_encoder = conv_encoder
        self.position_embedding = position_embedding

    def forward(self, pixel_values, pixel_mask):
        # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples
        out = self.conv_encoder(pixel_values, pixel_mask)
        pos = []
        for feature_map, mask in out:
            # position encoding
            pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype))

        return out, pos


class DeformableDetrSinePositionEmbedding(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
    need paper, generalized to work on images.
    """

    def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, pixel_values, pixel_mask):
        if pixel_mask is None:
            raise ValueError("No pixel mask provided")
        y_embed = pixel_mask.cumsum(1, dtype=pixel_values.dtype)
        x_embed = pixel_mask.cumsum(2, dtype=pixel_values.dtype)
        if self.normalize:
            eps = 1e-6
            y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.embedding_dim, dtype=pixel_values.dtype, device=pixel_values.device)
        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos


# Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding
class DeformableDetrLearnedPositionEmbedding(nn.Module):
    """
    This module learns positional embeddings up to a fixed maximum size.
    """

    def __init__(self, embedding_dim=256):
        super().__init__()
        self.row_embeddings = nn.Embedding(50, embedding_dim)
        self.column_embeddings = nn.Embedding(50, embedding_dim)

    def forward(self, pixel_values, pixel_mask=None):
        height, width = pixel_values.shape[-2:]
        width_values = torch.arange(width, device=pixel_values.device)
        height_values = torch.arange(height, device=pixel_values.device)
        x_emb = self.column_embeddings(width_values)
        y_emb = self.row_embeddings(height_values)
        pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
        pos = pos.permute(2, 0, 1)
        pos = pos.unsqueeze(0)
        pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
        return pos


# Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->DeformableDetr
def build_position_encoding(config):
    n_steps = config.d_model // 2
    if config.position_embedding_type == "sine":
        # TODO find a better way of exposing other arguments
        position_embedding = DeformableDetrSinePositionEmbedding(n_steps, normalize=True)
    elif config.position_embedding_type == "learned":
        position_embedding = DeformableDetrLearnedPositionEmbedding(n_steps)
    else:
        raise ValueError(f"Not supported {config.position_embedding_type}")

    return position_embedding


class DeformableDetrMultiscaleDeformableAttention(nn.Module):
    """
    Multiscale deformable attention as proposed in Deformable DETR.
    """

    def __init__(self, config: DeformableDetrConfig, num_heads: int, n_points: int):
        super().__init__()

        self.attn = MultiScaleDeformableAttention()

        if config.d_model % num_heads != 0:
            raise ValueError(
                f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}"
            )
        dim_per_head = config.d_model // num_heads
        # check if dim_per_head is power of 2
        if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
            warnings.warn(
                "You'd better set embed_dim (d_model) in DeformableDetrMultiscaleDeformableAttention to make the"
                " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
                " implementation."
            )

        self.im2col_step = 64

        self.d_model = config.d_model
        self.n_levels = config.num_feature_levels
        self.n_heads = num_heads
        self.n_points = n_points

        self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2)
        self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points)
        self.value_proj = nn.Linear(config.d_model, config.d_model)
        self.output_proj = nn.Linear(config.d_model, config.d_model)

        self.disable_custom_kernels = config.disable_custom_kernels

    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
        return tensor if position_embeddings is None else tensor + position_embeddings

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        output_attentions: bool = False,
    ):
        # add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

        batch_size, num_queries, _ = hidden_states.shape
        batch_size, sequence_length, _ = encoder_hidden_states.shape
        total_elements = sum(height * width for height, width in spatial_shapes_list)
        if total_elements != sequence_length:
            raise ValueError(
                "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
            )

        value = self.value_proj(encoder_hidden_states)
        if attention_mask is not None:
            # we invert the attention_mask
            value = value.masked_fill(~attention_mask[..., None], float(0))
        value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
        sampling_offsets = self.sampling_offsets(hidden_states).view(
            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
        )
        attention_weights = self.attention_weights(hidden_states).view(
            batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
        )
        attention_weights = F.softmax(attention_weights, -1).view(
            batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
        )
        # batch_size, num_queries, n_heads, n_levels, n_points, 2
        num_coordinates = reference_points.shape[-1]
        if num_coordinates == 2:
            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
            sampling_locations = (
                reference_points[:, :, None, :, None, :]
                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
            )
        elif num_coordinates == 4:
            sampling_locations = (
                reference_points[:, :, None, :, None, :2]
                + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
            )
        else:
            raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")

        output = self.attn(
            value,
            spatial_shapes,
            spatial_shapes_list,
            level_start_index,
            sampling_locations,
            attention_weights,
            self.im2col_step,
        )

        output = self.output_proj(output)

        return output, attention_weights


class DeformableDetrMultiheadAttention(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper.

    Here, we add position embeddings to the queries and keys (as explained in the Deformable DETR paper).
    """

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        if self.head_dim * num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {num_heads})."
            )
        self.scaling = self.head_dim**-0.5

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
        return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
        return tensor if position_embeddings is None else tensor + position_embeddings

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        batch_size, target_len, embed_dim = hidden_states.size()
        # add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states_original = hidden_states
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

        # get queries, keys and values
        query_states = self.q_proj(hidden_states) * self.scaling
        key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
        value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)

        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

        source_len = key_states.size(1)

        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
            raise ValueError(
                f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
                f" {attn_weights.size()}"
            )

        # expand attention_mask
        if attention_mask is not None:
            # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len]
            attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)

        if attention_mask is not None:
            if attention_mask.size() != (batch_size, 1, target_len, source_len):
                raise ValueError(
                    f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
                    f" {attention_mask.size()}"
                )
            if attention_mask.dtype == torch.bool:
                attention_mask = torch.zeros_like(attention_mask, dtype=attn_weights.dtype).masked_fill_(
                    attention_mask, -torch.inf
                )
            attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
            attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
            attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

        if attn_output.size() != (
            batch_size * self.num_heads,
            target_len,
            self.head_dim,
        ):
            raise ValueError(
                f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(batch_size, target_len, embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped


class DeformableDetrEncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: DeformableDetrConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = DeformableDetrMultiscaleDeformableAttention(
            config,
            num_heads=config.encoder_attention_heads,
            n_points=config.encoder_n_points,
        )
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        output_attentions: bool = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Input to the layer.
            attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Attention mask.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings, to be added to `hidden_states`.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes of the backbone feature maps.
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)

        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        if self.training:
            if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class DeformableDetrDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: DeformableDetrConfig):
        super().__init__()
        self.embed_dim = config.d_model

        # self-attention
        self.self_attn = DeformableDetrMultiheadAttention(
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        # cross-attention
        self.encoder_attn = DeformableDetrMultiscaleDeformableAttention(
            config,
            num_heads=config.decoder_attention_heads,
            n_points=config.decoder_n_points,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        # feedforward neural networks
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[torch.Tensor] = None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                Input to the layer of shape `(seq_len, batch, embed_dim)`.
            position_embeddings (`torch.FloatTensor`, *optional*):
                Position embeddings that are added to the queries and keys in the self-attention layer.
            reference_points (`torch.FloatTensor`, *optional*):
                Reference points.
            spatial_shapes (`torch.LongTensor`, *optional*):
                Spatial shapes.
            level_start_index (`torch.LongTensor`, *optional*):
                Level start index.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
                values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        second_residual = hidden_states

        # Cross-Attention
        cross_attn_weights = None
        hidden_states, cross_attn_weights = self.encoder_attn(
            hidden_states=hidden_states,
            attention_mask=encoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            position_embeddings=position_embeddings,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            output_attentions=output_attentions,
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = second_residual + hidden_states

        hidden_states = self.encoder_attn_layer_norm(hidden_states)

        # Fully Connected
        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        return outputs


@auto_docstring
class DeformableDetrPreTrainedModel(PreTrainedModel):
    config: DeformableDetrConfig
    base_model_prefix = "model"
    main_input_name = "pixel_values"
    input_modalities = "image"
    supports_gradient_checkpointing = True
    _no_split_modules = [
        r"DeformableDetrConvEncoder",
        r"DeformableDetrEncoderLayer",
        r"DeformableDetrDecoderLayer",
    ]

    @torch.no_grad()
    def _init_weights(self, module):
        std = self.config.init_std

        if isinstance(module, DeformableDetrLearnedPositionEmbedding):
            init.uniform_(module.row_embeddings.weight)
            init.uniform_(module.column_embeddings.weight)
        elif isinstance(module, DeformableDetrMultiscaleDeformableAttention):
            init.constant_(module.sampling_offsets.weight, 0.0)
            default_dtype = torch.get_default_dtype()
            thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * (
                2.0 * math.pi / module.n_heads
            )
            grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
            grid_init = (
                (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
                .view(module.n_heads, 1, 1, 2)
                .repeat(1, module.n_levels, module.n_points, 1)
            )
            for i in range(module.n_points):
                grid_init[:, :, i, :] *= i + 1

            init.copy_(module.sampling_offsets.bias, grid_init.view(-1))
            init.constant_(module.attention_weights.weight, 0.0)
            init.constant_(module.attention_weights.bias, 0.0)
            init.xavier_uniform_(module.value_proj.weight)
            init.constant_(module.value_proj.bias, 0.0)
            init.xavier_uniform_(module.output_proj.weight)
            init.constant_(module.output_proj.bias, 0.0)
        elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
            init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            init.normal_(module.weight, mean=0.0, std=std)
            # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag
            if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False):
                init.zeros_(module.weight[module.padding_idx])
        if hasattr(module, "reference_points") and not self.config.two_stage:
            init.xavier_uniform_(module.reference_points.weight, gain=1.0)
            init.constant_(module.reference_points.bias, 0.0)
        if hasattr(module, "level_embed"):
            init.normal_(module.level_embed)


class DeformableDetrEncoder(DeformableDetrPreTrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
    [`DeformableDetrEncoderLayer`].

    The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.

    Args:
        config: DeformableDetrConfig
    """

    def __init__(self, config: DeformableDetrConfig):
        super().__init__(config)
        self.gradient_checkpointing = False

        self.dropout = config.dropout
        self.layers = nn.ModuleList([DeformableDetrEncoderLayer(config) for _ in range(config.encoder_layers)])

        # Initialize weights and apply final processing
        self.post_init()

    @staticmethod
    def get_reference_points(spatial_shapes, valid_ratios, device):
        """
        Get reference points for each feature map. Used in decoder.

        Args:
            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of each feature map.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
                Valid ratios of each feature map.
            device (`torch.device`):
                Device on which to create the tensors.
        Returns:
            `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
        """
        reference_points_list = []
        for level, (height, width) in enumerate(spatial_shapes):
            ref_y, ref_x = meshgrid(
                torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device),
                torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device),
                indexing="ij",
            )
            # TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36
            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height)
            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width)
            ref = torch.stack((ref_x, ref_y), -1)
            reference_points_list.append(ref)
        reference_points = torch.cat(reference_points_list, 1)
        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
        return reference_points

    def forward(
        self,
        inputs_embeds=None,
        attention_mask=None,
        position_embeddings=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        valid_ratios=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
                - 1 for pixel features that are real (i.e. **not masked**),
                - 0 for pixel features that are padding (i.e. **masked**).
                [What are attention masks?](../glossary#attention-mask)
            position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Position embeddings that are added to the queries and keys in each self-attention layer.
            spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of each feature map.
            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
                Starting index of each feature map.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
                Ratio of valid area in each feature level.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        hidden_states = inputs_embeds
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        spatial_shapes_tuple = tuple(spatial_shapes_list)
        reference_points = self.get_reference_points(spatial_shapes_tuple, valid_ratios, device=inputs_embeds.device)

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            layer_outputs = encoder_layer(
                hidden_states,
                attention_mask,
                position_embeddings=position_embeddings,
                reference_points=reference_points,
                spatial_shapes=spatial_shapes,
                spatial_shapes_list=spatial_shapes_list,
                level_start_index=level_start_index,
                output_attentions=output_attentions,
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=encoder_states,
            attentions=all_attentions,
        )


class DeformableDetrDecoder(DeformableDetrPreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DeformableDetrDecoderLayer`].

    The decoder updates the query embeddings through multiple self-attention and cross-attention layers.

    Some tweaks for Deformable DETR:

    - `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass.
    - it also returns a stack of intermediate outputs and reference points from all decoding layers.

    Args:
        config: DeformableDetrConfig
    """

    def __init__(self, config: DeformableDetrConfig):
        super().__init__(config)

        self.dropout = config.dropout
        self.layers = nn.ModuleList([DeformableDetrDecoderLayer(config) for _ in range(config.decoder_layers)])
        self.gradient_checkpointing = False

        # hack implementation for iterative bounding box refinement and two-stage Deformable DETR
        self.bbox_embed = None
        self.class_embed = None

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        position_embeddings=None,
        reference_points=None,
        spatial_shapes=None,
        spatial_shapes_list=None,
        level_start_index=None,
        valid_ratios=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
                The query embeddings that are passed into the decoder.
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
                of the decoder.
            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
                in `[0, 1]`:
                - 1 for pixels that are real (i.e. **not masked**),
                - 0 for pixels that are padding (i.e. **masked**).
            position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
                Position embeddings that are added to the queries and keys in each self-attention layer.
            reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*):
                Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area.
            spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`):
                Spatial shapes of the feature maps.
            level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*):
                Indexes for the start of each feature level. In range `[0, sequence_length]`.
            valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*):
                Ratio of valid area in each feature level.

            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if inputs_embeds is not None:
            hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        intermediate = ()
        intermediate_reference_points = ()

        for idx, decoder_layer in enumerate(self.layers):
            num_coordinates = reference_points.shape[-1]
            if num_coordinates == 4:
                reference_points_input = (
                    reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
                )
            elif reference_points.shape[-1] == 2:
                reference_points_input = reference_points[:, :, None] * valid_ratios[:, None]
            else:
                raise ValueError("Reference points' last dimension must be of size 2")

            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = decoder_layer(
                hidden_states,
                position_embeddings,
                reference_points_input,
                spatial_shapes,
                spatial_shapes_list,
                level_start_index,
                encoder_hidden_states,  # as a positional argument for gradient checkpointing
                encoder_attention_mask,
                output_attentions,
            )

            hidden_states = layer_outputs[0]

            # hack implementation for iterative bounding box refinement
            if self.bbox_embed is not None:
                tmp = self.bbox_embed[idx](hidden_states)
                num_coordinates = reference_points.shape[-1]
                if num_coordinates == 4:
                    new_reference_points = tmp + inverse_sigmoid(reference_points)
                    new_reference_points = new_reference_points.sigmoid()
                elif num_coordinates == 2:
                    new_reference_points = tmp
                    new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)
                    new_reference_points = new_reference_points.sigmoid()
                else:
                    raise ValueError(
                        f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}"
                    )
                reference_points = new_reference_points.detach()

            intermediate += (hidden_states,)
            intermediate_reference_points += (reference_points,)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        # Keep batch_size as first dimension
        intermediate = torch.stack(intermediate, dim=1)
        intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    intermediate,
                    intermediate_reference_points,
                    all_hidden_states,
                    all_self_attns,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return DeformableDetrDecoderOutput(
            last_hidden_state=hidden_states,
            intermediate_hidden_states=intermediate,
            intermediate_reference_points=intermediate_reference_points,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


@auto_docstring(
    custom_intro="""
    The bare Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
    hidden-states without any specific head on top.
    """
)
class DeformableDetrModel(DeformableDetrPreTrainedModel):
    def __init__(self, config: DeformableDetrConfig):
        super().__init__(config)

        # Create backbone + positional encoding
        backbone = DeformableDetrConvEncoder(config)
        position_embeddings = build_position_encoding(config)
        self.backbone = DeformableDetrConvModel(backbone, position_embeddings)

        # Create input projection layers
        if config.num_feature_levels > 1:
            num_backbone_outs = len(backbone.intermediate_channel_sizes)
            input_proj_list = []
            for _ in range(num_backbone_outs):
                in_channels = backbone.intermediate_channel_sizes[_]
                input_proj_list.append(
                    nn.Sequential(
                        nn.Conv2d(in_channels, config.d_model, kernel_size=1),
                        nn.GroupNorm(32, config.d_model),
                    )
                )
            for _ in range(config.num_feature_levels - num_backbone_outs):
                input_proj_list.append(
                    nn.Sequential(
                        nn.Conv2d(
                            in_channels,
                            config.d_model,
                            kernel_size=3,
                            stride=2,
                            padding=1,
                        ),
                        nn.GroupNorm(32, config.d_model),
                    )
                )
                in_channels = config.d_model
            self.input_proj = nn.ModuleList(input_proj_list)
        else:
            self.input_proj = nn.ModuleList(
                [
                    nn.Sequential(
                        nn.Conv2d(
                            backbone.intermediate_channel_sizes[-1],
                            config.d_model,
                            kernel_size=1,
                        ),
                        nn.GroupNorm(32, config.d_model),
                    )
                ]
            )

        if not config.two_stage:
            self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model * 2)

        self.encoder = DeformableDetrEncoder(config)
        self.decoder = DeformableDetrDecoder(config)

        self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))

        if config.two_stage:
            self.enc_output = nn.Linear(config.d_model, config.d_model)
            self.enc_output_norm = nn.LayerNorm(config.d_model)
            self.pos_trans = nn.Linear(config.d_model * 2, config.d_model * 2)
            self.pos_trans_norm = nn.LayerNorm(config.d_model * 2)
        else:
            self.reference_points = nn.Linear(config.d_model, 2)

        self.post_init()

    def freeze_backbone(self):
        for name, param in self.backbone.conv_encoder.model.named_parameters():
            param.requires_grad_(False)

    def unfreeze_backbone(self):
        for name, param in self.backbone.conv_encoder.model.named_parameters():
            param.requires_grad_(True)

    def get_valid_ratio(self, mask, dtype=torch.float32):
        """Get the valid ratio of all feature maps."""

        _, height, width = mask.shape
        valid_height = torch.sum(mask[:, :, 0], 1)
        valid_width = torch.sum(mask[:, 0, :], 1)
        valid_ratio_height = valid_height.to(dtype) / height
        valid_ratio_width = valid_width.to(dtype) / width
        valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
        return valid_ratio

    def get_proposal_pos_embed(self, proposals):
        """Get the position embedding of the proposals."""

        num_pos_feats = self.config.d_model // 2
        temperature = 10000
        scale = 2 * math.pi

        dim_t = torch.arange(num_pos_feats, dtype=proposals.dtype, device=proposals.device)
        dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
        # batch_size, num_queries, 4
        proposals = proposals.sigmoid() * scale
        # batch_size, num_queries, 4, 128
        pos = proposals[:, :, :, None] / dim_t
        # batch_size, num_queries, 4, 64, 2 -> batch_size, num_queries, 512
        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
        return pos

    def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes):
        """Generate the encoder output proposals from encoded enc_output.

        Args:
            enc_output (Tensor[batch_size, sequence_length, hidden_size]): Output of the encoder.
            padding_mask (Tensor[batch_size, sequence_length]): Padding mask for `enc_output`.
            spatial_shapes (list[tuple[int, int]]): Spatial shapes of the feature maps.

        Returns:
            `tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction.
                - object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to
                  directly predict a bounding box. (without the need of a decoder)
                - output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse
                  sigmoid.
        """
        batch_size = enc_output.shape[0]
        proposals = []
        _cur = 0
        for level, (height, width) in enumerate(spatial_shapes):
            mask_flatten_ = padding_mask[:, _cur : (_cur + height * width)].view(batch_size, height, width, 1)
            valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
            valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1)

            grid_y, grid_x = meshgrid(
                torch.linspace(
                    0,
                    height - 1,
                    height,
                    dtype=enc_output.dtype,
                    device=enc_output.device,
                ),
                torch.linspace(
                    0,
                    width - 1,
                    width,
                    dtype=enc_output.dtype,
                    device=enc_output.device,
                ),
                indexing="ij",
            )
            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)

            scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2)
            grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale
            width_height = torch.ones_like(grid) * 0.05 * (2.0**level)
            proposal = torch.cat((grid, width_height), -1).view(batch_size, -1, 4)
            proposals.append(proposal)
            _cur += height * width
        output_proposals = torch.cat(proposals, 1)
        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
        output_proposals = torch.log(output_proposals / (1 - output_proposals))  # inverse sigmoid
        output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf"))
        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))

        # assign each pixel as an object query
        object_query = enc_output
        object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0))
        object_query = object_query.masked_fill(~output_proposals_valid, float(0))
        object_query = self.enc_output_norm(self.enc_output(object_query))
        return object_query, output_proposals

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        pixel_mask: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.FloatTensor] = None,
        encoder_outputs: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple[torch.FloatTensor], DeformableDetrModelOutput]:
        r"""
        decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*):
            Not used by default. Can be used to mask object queries.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
            can choose to directly pass a flattened representation of an image.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
            Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
            embedded representation.

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, DeformableDetrModel
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr")
        >>> model = DeformableDetrModel.from_pretrained("SenseTime/deformable-detr")

        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 300, 256]
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        batch_size, num_channels, height, width = pixel_values.shape
        device = pixel_values.device

        if pixel_mask is None:
            pixel_mask = torch.ones(((batch_size, height, width)), dtype=torch.long, device=device)

        # Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper)
        # First, sent pixel_values + pixel_mask through Backbone to obtain the features
        # which is a list of tuples
        features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)

        # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
        sources = []
        masks = []
        for level, (source, mask) in enumerate(features):
            sources.append(self.input_proj[level](source))
            masks.append(mask)
            if mask is None:
                raise ValueError("No attention mask was provided")

        # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage
        if self.config.num_feature_levels > len(sources):
            _len_sources = len(sources)
            for level in range(_len_sources, self.config.num_feature_levels):
                if level == _len_sources:
                    source = self.input_proj[level](features[-1][0])
                else:
                    source = self.input_proj[level](sources[-1])
                mask = nn.functional.interpolate(pixel_mask[None].to(pixel_values.dtype), size=source.shape[-2:]).to(
                    torch.bool
                )[0]
                pos_l = self.backbone.position_embedding(source, mask).to(source.dtype)
                sources.append(source)
                masks.append(mask)
                position_embeddings_list.append(pos_l)

        # Create queries
        query_embeds = None
        if not self.config.two_stage:
            query_embeds = self.query_position_embeddings.weight

        # Prepare encoder inputs (by flattening)
        source_flatten = []
        mask_flatten = []
        lvl_pos_embed_flatten = []
        spatial_shapes_list = []
        for level, (source, mask, pos_embed) in enumerate(zip(sources, masks, position_embeddings_list)):
            batch_size, num_channels, height, width = source.shape
            spatial_shape = (height, width)
            spatial_shapes_list.append(spatial_shape)
            source = source.flatten(2).transpose(1, 2)
            mask = mask.flatten(1)
            pos_embed = pos_embed.flatten(2).transpose(1, 2)
            lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1)
            lvl_pos_embed_flatten.append(lvl_pos_embed)
            source_flatten.append(source)
            mask_flatten.append(mask)
        source_flatten = torch.cat(source_flatten, 1)
        mask_flatten = torch.cat(mask_flatten, 1)
        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
        spatial_shapes = torch.as_tensor(spatial_shapes_list, dtype=torch.long, device=source_flatten.device)
        level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
        valid_ratios = torch.stack([self.get_valid_ratio(m, dtype=source_flatten.dtype) for m in masks], 1)

        # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder
        # Also provide spatial_shapes, level_start_index and valid_ratios
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                inputs_embeds=source_flatten,
                attention_mask=mask_flatten,
                position_embeddings=lvl_pos_embed_flatten,
                spatial_shapes=spatial_shapes,
                spatial_shapes_list=spatial_shapes_list,
                level_start_index=level_start_index,
                valid_ratios=valid_ratios,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        # Fifth, prepare decoder inputs
        batch_size, _, num_channels = encoder_outputs[0].shape
        enc_outputs_class = None
        enc_outputs_coord_logits = None
        if self.config.two_stage:
            object_query_embedding, output_proposals = self.gen_encoder_output_proposals(
                encoder_outputs[0], ~mask_flatten, spatial_shapes_list
            )

            # hack implementation for two-stage Deformable DETR
            # apply a detection head to each pixel (A.4 in paper)
            # linear projection for bounding box binary classification (i.e. foreground and background)
            enc_outputs_class = self.decoder.class_embed[-1](object_query_embedding)
            # 3-layer FFN to predict bounding boxes coordinates (bbox regression branch)
            delta_bbox = self.decoder.bbox_embed[-1](object_query_embedding)
            enc_outputs_coord_logits = delta_bbox + output_proposals

            # only keep top scoring `config.two_stage_num_proposals` proposals
            topk = self.config.two_stage_num_proposals
            topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]
            topk_coords_logits = torch.gather(
                enc_outputs_coord_logits,
                1,
                topk_proposals.unsqueeze(-1).repeat(1, 1, 4),
            )

            topk_coords_logits = topk_coords_logits.detach()
            reference_points = topk_coords_logits.sigmoid()
            init_reference_points = reference_points
            pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_logits)))
            query_embed, target = torch.split(pos_trans_out, num_channels, dim=2)
        else:
            query_embed, target = torch.split(query_embeds, num_channels, dim=1)
            query_embed = query_embed.unsqueeze(0).expand(batch_size, -1, -1)
            target = target.unsqueeze(0).expand(batch_size, -1, -1)
            reference_points = self.reference_points(query_embed).sigmoid()
            init_reference_points = reference_points

        decoder_outputs = self.decoder(
            inputs_embeds=target,
            position_embeddings=query_embed,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=mask_flatten,
            reference_points=reference_points,
            spatial_shapes=spatial_shapes,
            spatial_shapes_list=spatial_shapes_list,
            level_start_index=level_start_index,
            valid_ratios=valid_ratios,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            enc_outputs = tuple(value for value in [enc_outputs_class, enc_outputs_coord_logits] if value is not None)
            tuple_outputs = (init_reference_points,) + decoder_outputs + encoder_outputs + enc_outputs

            return tuple_outputs

        return DeformableDetrModelOutput(
            init_reference_points=init_reference_points,
            last_hidden_state=decoder_outputs.last_hidden_state,
            intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
            intermediate_reference_points=decoder_outputs.intermediate_reference_points,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            enc_outputs_class=enc_outputs_class,
            enc_outputs_coord_logits=enc_outputs_coord_logits,
        )


# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead
class DeformableDetrMLPPredictionHead(nn.Module):
    """
    Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
    height and width of a bounding box w.r.t. an image.

    Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py

    """

    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
        return x


@auto_docstring(
    custom_intro="""
    Deformable DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on
    top, for tasks such as COCO detection.
    """
)
class DeformableDetrForObjectDetection(DeformableDetrPreTrainedModel):
    # When using clones, all layers > 0 will be clones, but layer 0 *is* required
    # We can't initialize the model on meta device as some weights are modified during the initialization
    _no_split_modules = None
    _tied_weights_keys = {
        r"bbox_embed.(?![0])\d+": "bbox_embed.0",
        r"class_embed.(?![0])\d+": "class_embed.0",
    }

    def __init__(self, config: DeformableDetrConfig):
        super().__init__(config)
        # Deformable DETR encoder-decoder model
        self.model = DeformableDetrModel(config)
        # Detection heads on top
        # if two-stage, the last class_embed and bbox_embed is for region proposal generation
        num_pred = (config.decoder_layers + 1) if config.two_stage else config.decoder_layers
        self.class_embed = nn.ModuleList([nn.Linear(config.d_model, config.num_labels) for _ in range(num_pred)])
        self.bbox_embed = nn.ModuleList(
            [
                DeformableDetrMLPPredictionHead(
                    input_dim=config.d_model,
                    hidden_dim=config.d_model,
                    output_dim=4,
                    num_layers=3,
                )
                for _ in range(num_pred)
            ]
        )
        if config.with_box_refine:
            self.model.decoder.bbox_embed = self.bbox_embed
            self._tied_weights_keys["model.decoder.bbox_embed"] = "bbox_embed"
        if config.two_stage:
            self.model.decoder.class_embed = self.class_embed
            self._tied_weights_keys["model.decoder.class_embed"] = "class_embed"
        self.post_init()

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        pixel_mask: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.FloatTensor] = None,
        encoder_outputs: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[list[dict]] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple[torch.FloatTensor], DeformableDetrObjectDetectionOutput]:
        r"""
        decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*):
            Not used by default. Can be used to mask object queries.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
            can choose to directly pass a flattened representation of an image.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
            Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
            embedded representation.
        labels (`list[Dict]` of len `(batch_size,)`, *optional*):
            Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
            following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
            respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
            in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, DeformableDetrForObjectDetection
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr")
        >>> model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr")

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
        >>> target_sizes = torch.tensor([image.size[::-1]])
        >>> results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[
        ...     0
        ... ]
        >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        ...     box = [round(i, 2) for i in box.tolist()]
        ...     print(
        ...         f"Detected {model.config.id2label[label.item()]} with confidence "
        ...         f"{round(score.item(), 3)} at location {box}"
        ...     )
        Detected cat with confidence 0.8 at location [16.5, 52.84, 318.25, 470.78]
        Detected cat with confidence 0.789 at location [342.19, 24.3, 640.02, 372.25]
        Detected remote with confidence 0.633 at location [40.79, 72.78, 176.76, 117.25]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # First, sent images through DETR base model to obtain encoder + decoder outputs
        outputs = self.model(
            pixel_values,
            pixel_mask=pixel_mask,
            decoder_attention_mask=decoder_attention_mask,
            encoder_outputs=encoder_outputs,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2]
        init_reference = outputs.init_reference_points if return_dict else outputs[0]
        inter_references = outputs.intermediate_reference_points if return_dict else outputs[3]

        # class logits + predicted bounding boxes
        outputs_classes = []
        outputs_coords = []

        for level in range(hidden_states.shape[1]):
            if level == 0:
                reference = init_reference
            else:
                reference = inter_references[:, level - 1]
            reference = inverse_sigmoid(reference)
            outputs_class = self.class_embed[level](hidden_states[:, level])
            delta_bbox = self.bbox_embed[level](hidden_states[:, level])
            if reference.shape[-1] == 4:
                outputs_coord_logits = delta_bbox + reference
            elif reference.shape[-1] == 2:
                delta_bbox[..., :2] += reference
                outputs_coord_logits = delta_bbox
            else:
                raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}")
            outputs_coord = outputs_coord_logits.sigmoid()
            outputs_classes.append(outputs_class)
            outputs_coords.append(outputs_coord)
        outputs_class = torch.stack(outputs_classes)
        outputs_coord = torch.stack(outputs_coords)

        logits = outputs_class[-1]
        pred_boxes = outputs_coord[-1]

        loss, loss_dict, auxiliary_outputs = None, None, None
        if labels is not None:
            loss, loss_dict, auxiliary_outputs = self.loss_function(
                logits,
                labels,
                self.device,
                pred_boxes,
                self.config,
                outputs_class,
                outputs_coord,
            )
        if not return_dict:
            if auxiliary_outputs is not None:
                output = (logits, pred_boxes) + auxiliary_outputs + outputs
            else:
                output = (logits, pred_boxes) + outputs
            tuple_outputs = ((loss, loss_dict) + output) if loss is not None else output

            return tuple_outputs

        dict_outputs = DeformableDetrObjectDetectionOutput(
            loss=loss,
            loss_dict=loss_dict,
            logits=logits,
            pred_boxes=pred_boxes,
            auxiliary_outputs=auxiliary_outputs,
            last_hidden_state=outputs.last_hidden_state,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
            intermediate_hidden_states=outputs.intermediate_hidden_states,
            intermediate_reference_points=outputs.intermediate_reference_points,
            init_reference_points=outputs.init_reference_points,
            enc_outputs_class=outputs.enc_outputs_class,
            enc_outputs_coord_logits=outputs.enc_outputs_coord_logits,
        )

        return dict_outputs


__all__ = [
    "DeformableDetrForObjectDetection",
    "DeformableDetrModel",
    "DeformableDetrPreTrainedModel",
]
